{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 股票数据分析\n",
    "\n",
    "具体详见 https://github.com/kamidox/stock-analysis\n",
    "\n",
    "这里假设数据已经下载下来，并且保存在 yahoo-data 目录下。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析波动幅度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-20</th>\n",
       "      <td>18.82000</td>\n",
       "      <td>19.46000</td>\n",
       "      <td>18.71000</td>\n",
       "      <td>19.34000</td>\n",
       "      <td>13265400</td>\n",
       "      <td>19.34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-19</th>\n",
       "      <td>19.12000</td>\n",
       "      <td>19.52000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>18.98000</td>\n",
       "      <td>12581300</td>\n",
       "      <td>18.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-18</th>\n",
       "      <td>19.50000</td>\n",
       "      <td>20.10000</td>\n",
       "      <td>18.83000</td>\n",
       "      <td>19.23000</td>\n",
       "      <td>22042500</td>\n",
       "      <td>19.23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-17</th>\n",
       "      <td>19.73000</td>\n",
       "      <td>20.23000</td>\n",
       "      <td>19.65000</td>\n",
       "      <td>19.77000</td>\n",
       "      <td>20469800</td>\n",
       "      <td>19.77000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-16</th>\n",
       "      <td>19.43000</td>\n",
       "      <td>19.64000</td>\n",
       "      <td>19.20000</td>\n",
       "      <td>19.62000</td>\n",
       "      <td>10963200</td>\n",
       "      <td>19.62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-13</th>\n",
       "      <td>19.70000</td>\n",
       "      <td>19.94000</td>\n",
       "      <td>19.27000</td>\n",
       "      <td>19.40000</td>\n",
       "      <td>15655100</td>\n",
       "      <td>19.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-12</th>\n",
       "      <td>18.81000</td>\n",
       "      <td>19.95000</td>\n",
       "      <td>18.71000</td>\n",
       "      <td>19.88000</td>\n",
       "      <td>19814300</td>\n",
       "      <td>19.88000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-11</th>\n",
       "      <td>19.50000</td>\n",
       "      <td>19.85000</td>\n",
       "      <td>19.12000</td>\n",
       "      <td>19.28000</td>\n",
       "      <td>23742200</td>\n",
       "      <td>19.28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-10</th>\n",
       "      <td>18.86000</td>\n",
       "      <td>19.17000</td>\n",
       "      <td>18.23000</td>\n",
       "      <td>19.07000</td>\n",
       "      <td>20858200</td>\n",
       "      <td>19.07000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>18.62000</td>\n",
       "      <td>18.85000</td>\n",
       "      <td>17.89000</td>\n",
       "      <td>18.67000</td>\n",
       "      <td>22525400</td>\n",
       "      <td>18.67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-06</th>\n",
       "      <td>20.49000</td>\n",
       "      <td>20.49000</td>\n",
       "      <td>18.69000</td>\n",
       "      <td>18.70000</td>\n",
       "      <td>40962500</td>\n",
       "      <td>18.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-05</th>\n",
       "      <td>20.15000</td>\n",
       "      <td>20.57000</td>\n",
       "      <td>20.01000</td>\n",
       "      <td>20.49000</td>\n",
       "      <td>15216600</td>\n",
       "      <td>20.49000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-04</th>\n",
       "      <td>20.80000</td>\n",
       "      <td>21.10000</td>\n",
       "      <td>20.20000</td>\n",
       "      <td>20.42000</td>\n",
       "      <td>22555400</td>\n",
       "      <td>20.42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-03</th>\n",
       "      <td>20.60000</td>\n",
       "      <td>20.99000</td>\n",
       "      <td>19.90000</td>\n",
       "      <td>20.99000</td>\n",
       "      <td>35319300</td>\n",
       "      <td>20.99000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-02</th>\n",
       "      <td>20.61000</td>\n",
       "      <td>20.61000</td>\n",
       "      <td>20.61000</td>\n",
       "      <td>20.61000</td>\n",
       "      <td>0</td>\n",
       "      <td>20.61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-29</th>\n",
       "      <td>20.20000</td>\n",
       "      <td>20.77000</td>\n",
       "      <td>19.85000</td>\n",
       "      <td>20.61000</td>\n",
       "      <td>17845800</td>\n",
       "      <td>20.61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-28</th>\n",
       "      <td>21.49000</td>\n",
       "      <td>21.50000</td>\n",
       "      <td>19.60000</td>\n",
       "      <td>20.40000</td>\n",
       "      <td>41130000</td>\n",
       "      <td>20.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-27</th>\n",
       "      <td>21.25000</td>\n",
       "      <td>22.08000</td>\n",
       "      <td>21.12000</td>\n",
       "      <td>21.44000</td>\n",
       "      <td>31398800</td>\n",
       "      <td>21.44000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-26</th>\n",
       "      <td>20.90000</td>\n",
       "      <td>21.27000</td>\n",
       "      <td>20.41000</td>\n",
       "      <td>21.13000</td>\n",
       "      <td>19271100</td>\n",
       "      <td>21.13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-25</th>\n",
       "      <td>21.61000</td>\n",
       "      <td>21.61000</td>\n",
       "      <td>20.57000</td>\n",
       "      <td>20.92000</td>\n",
       "      <td>19571900</td>\n",
       "      <td>20.92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-22</th>\n",
       "      <td>21.44000</td>\n",
       "      <td>21.92000</td>\n",
       "      <td>21.01000</td>\n",
       "      <td>21.71000</td>\n",
       "      <td>27411200</td>\n",
       "      <td>21.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-21</th>\n",
       "      <td>20.06000</td>\n",
       "      <td>21.97000</td>\n",
       "      <td>19.81000</td>\n",
       "      <td>21.42000</td>\n",
       "      <td>45631800</td>\n",
       "      <td>21.42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-20</th>\n",
       "      <td>20.68000</td>\n",
       "      <td>21.30000</td>\n",
       "      <td>19.42000</td>\n",
       "      <td>19.97000</td>\n",
       "      <td>34444400</td>\n",
       "      <td>19.97000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-19</th>\n",
       "      <td>20.91000</td>\n",
       "      <td>21.23000</td>\n",
       "      <td>20.35000</td>\n",
       "      <td>20.55000</td>\n",
       "      <td>16482800</td>\n",
       "      <td>20.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-18</th>\n",
       "      <td>20.55000</td>\n",
       "      <td>21.20000</td>\n",
       "      <td>19.99000</td>\n",
       "      <td>20.91000</td>\n",
       "      <td>24520700</td>\n",
       "      <td>20.91000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-15</th>\n",
       "      <td>20.38000</td>\n",
       "      <td>20.73000</td>\n",
       "      <td>20.30000</td>\n",
       "      <td>20.71000</td>\n",
       "      <td>16271200</td>\n",
       "      <td>20.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-14</th>\n",
       "      <td>20.60000</td>\n",
       "      <td>20.83000</td>\n",
       "      <td>19.92000</td>\n",
       "      <td>20.40000</td>\n",
       "      <td>26698500</td>\n",
       "      <td>20.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-13</th>\n",
       "      <td>20.62000</td>\n",
       "      <td>21.17000</td>\n",
       "      <td>20.49000</td>\n",
       "      <td>20.50000</td>\n",
       "      <td>25012500</td>\n",
       "      <td>20.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-12</th>\n",
       "      <td>21.50000</td>\n",
       "      <td>21.50000</td>\n",
       "      <td>20.10000</td>\n",
       "      <td>20.49000</td>\n",
       "      <td>29331800</td>\n",
       "      <td>20.49000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-11</th>\n",
       "      <td>21.94000</td>\n",
       "      <td>22.30000</td>\n",
       "      <td>21.54000</td>\n",
       "      <td>21.57000</td>\n",
       "      <td>24064600</td>\n",
       "      <td>21.57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-05</th>\n",
       "      <td>17.90003</td>\n",
       "      <td>18.10002</td>\n",
       "      <td>17.52999</td>\n",
       "      <td>17.56000</td>\n",
       "      <td>8750200</td>\n",
       "      <td>0.98194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-04</th>\n",
       "      <td>17.86002</td>\n",
       "      <td>18.01999</td>\n",
       "      <td>17.49998</td>\n",
       "      <td>17.83001</td>\n",
       "      <td>10656000</td>\n",
       "      <td>0.99704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-03</th>\n",
       "      <td>17.58001</td>\n",
       "      <td>18.12002</td>\n",
       "      <td>17.58001</td>\n",
       "      <td>17.73998</td>\n",
       "      <td>12815400</td>\n",
       "      <td>0.99201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-02</th>\n",
       "      <td>17.44996</td>\n",
       "      <td>17.88003</td>\n",
       "      <td>17.21997</td>\n",
       "      <td>17.61002</td>\n",
       "      <td>10593500</td>\n",
       "      <td>0.98474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-30</th>\n",
       "      <td>17.47997</td>\n",
       "      <td>17.80000</td>\n",
       "      <td>17.20997</td>\n",
       "      <td>17.50999</td>\n",
       "      <td>12284500</td>\n",
       "      <td>0.97914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-29</th>\n",
       "      <td>17.15003</td>\n",
       "      <td>17.90003</td>\n",
       "      <td>16.99998</td>\n",
       "      <td>17.46997</td>\n",
       "      <td>15769400</td>\n",
       "      <td>0.97691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-28</th>\n",
       "      <td>16.32001</td>\n",
       "      <td>16.95997</td>\n",
       "      <td>16.13003</td>\n",
       "      <td>16.92996</td>\n",
       "      <td>5868400</td>\n",
       "      <td>0.94671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-27</th>\n",
       "      <td>16.90004</td>\n",
       "      <td>16.99998</td>\n",
       "      <td>15.99998</td>\n",
       "      <td>16.30000</td>\n",
       "      <td>7869100</td>\n",
       "      <td>0.91148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-26</th>\n",
       "      <td>17.19996</td>\n",
       "      <td>17.21997</td>\n",
       "      <td>16.55000</td>\n",
       "      <td>16.80000</td>\n",
       "      <td>7509400</td>\n",
       "      <td>0.93944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-23</th>\n",
       "      <td>17.01999</td>\n",
       "      <td>17.24998</td>\n",
       "      <td>16.62002</td>\n",
       "      <td>17.21997</td>\n",
       "      <td>8398200</td>\n",
       "      <td>0.96293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-22</th>\n",
       "      <td>17.71997</td>\n",
       "      <td>17.94996</td>\n",
       "      <td>16.91004</td>\n",
       "      <td>17.02999</td>\n",
       "      <td>8947200</td>\n",
       "      <td>0.95230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-21</th>\n",
       "      <td>17.93996</td>\n",
       "      <td>18.16004</td>\n",
       "      <td>17.59001</td>\n",
       "      <td>17.60002</td>\n",
       "      <td>9462400</td>\n",
       "      <td>0.98418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-20</th>\n",
       "      <td>17.99998</td>\n",
       "      <td>18.32001</td>\n",
       "      <td>17.74998</td>\n",
       "      <td>17.86002</td>\n",
       "      <td>16607900</td>\n",
       "      <td>0.99872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-19</th>\n",
       "      <td>17.39003</td>\n",
       "      <td>18.33001</td>\n",
       "      <td>17.23998</td>\n",
       "      <td>18.05000</td>\n",
       "      <td>23611100</td>\n",
       "      <td>1.00934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-16</th>\n",
       "      <td>16.51999</td>\n",
       "      <td>17.67004</td>\n",
       "      <td>16.45997</td>\n",
       "      <td>17.40003</td>\n",
       "      <td>20279700</td>\n",
       "      <td>0.97300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-15</th>\n",
       "      <td>16.81000</td>\n",
       "      <td>17.22997</td>\n",
       "      <td>16.50999</td>\n",
       "      <td>16.70997</td>\n",
       "      <td>9443200</td>\n",
       "      <td>0.93441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-14</th>\n",
       "      <td>16.69997</td>\n",
       "      <td>16.90004</td>\n",
       "      <td>16.15004</td>\n",
       "      <td>16.89003</td>\n",
       "      <td>9665300</td>\n",
       "      <td>0.94448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-13</th>\n",
       "      <td>16.12002</td>\n",
       "      <td>16.80000</td>\n",
       "      <td>15.80000</td>\n",
       "      <td>16.80000</td>\n",
       "      <td>10745200</td>\n",
       "      <td>0.93944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-12</th>\n",
       "      <td>17.35002</td>\n",
       "      <td>17.35002</td>\n",
       "      <td>16.20997</td>\n",
       "      <td>16.42996</td>\n",
       "      <td>17923100</td>\n",
       "      <td>0.91875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-09</th>\n",
       "      <td>17.63003</td>\n",
       "      <td>18.17996</td>\n",
       "      <td>17.31000</td>\n",
       "      <td>17.47997</td>\n",
       "      <td>10998800</td>\n",
       "      <td>0.97747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-08</th>\n",
       "      <td>17.17004</td>\n",
       "      <td>17.66004</td>\n",
       "      <td>17.10002</td>\n",
       "      <td>17.49998</td>\n",
       "      <td>7263400</td>\n",
       "      <td>0.97858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-07</th>\n",
       "      <td>17.80000</td>\n",
       "      <td>17.80000</td>\n",
       "      <td>17.13003</td>\n",
       "      <td>17.27999</td>\n",
       "      <td>13155000</td>\n",
       "      <td>0.96628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-06</th>\n",
       "      <td>17.80000</td>\n",
       "      <td>18.24998</td>\n",
       "      <td>17.51999</td>\n",
       "      <td>17.85002</td>\n",
       "      <td>17018000</td>\n",
       "      <td>0.99816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-05</th>\n",
       "      <td>17.49998</td>\n",
       "      <td>17.93996</td>\n",
       "      <td>17.12002</td>\n",
       "      <td>17.73998</td>\n",
       "      <td>17707600</td>\n",
       "      <td>0.99201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-02</th>\n",
       "      <td>18.76999</td>\n",
       "      <td>19.10002</td>\n",
       "      <td>16.99998</td>\n",
       "      <td>17.67996</td>\n",
       "      <td>37618100</td>\n",
       "      <td>0.98865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-01</th>\n",
       "      <td>18.90003</td>\n",
       "      <td>19.35002</td>\n",
       "      <td>18.65003</td>\n",
       "      <td>18.76999</td>\n",
       "      <td>32947700</td>\n",
       "      <td>1.04960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-06-30</th>\n",
       "      <td>18.25998</td>\n",
       "      <td>19.99998</td>\n",
       "      <td>18.25998</td>\n",
       "      <td>19.02999</td>\n",
       "      <td>54561100</td>\n",
       "      <td>1.06414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-06-29</th>\n",
       "      <td>20.27999</td>\n",
       "      <td>20.27999</td>\n",
       "      <td>20.27999</td>\n",
       "      <td>20.27999</td>\n",
       "      <td>3475700</td>\n",
       "      <td>1.13404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-06-28</th>\n",
       "      <td>22.52999</td>\n",
       "      <td>22.99998</td>\n",
       "      <td>22.52999</td>\n",
       "      <td>22.52999</td>\n",
       "      <td>7168200</td>\n",
       "      <td>1.25986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-06-25</th>\n",
       "      <td>28.10001</td>\n",
       "      <td>29.99997</td>\n",
       "      <td>23.99997</td>\n",
       "      <td>25.02999</td>\n",
       "      <td>177992600</td>\n",
       "      <td>1.39966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3057 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close     Volume  Adj Close\n",
       "Date                                                                    \n",
       "2016-05-20  18.82000  19.46000  18.71000  19.34000   13265400   19.34000\n",
       "2016-05-19  19.12000  19.52000  18.90000  18.98000   12581300   18.98000\n",
       "2016-05-18  19.50000  20.10000  18.83000  19.23000   22042500   19.23000\n",
       "2016-05-17  19.73000  20.23000  19.65000  19.77000   20469800   19.77000\n",
       "2016-05-16  19.43000  19.64000  19.20000  19.62000   10963200   19.62000\n",
       "2016-05-13  19.70000  19.94000  19.27000  19.40000   15655100   19.40000\n",
       "2016-05-12  18.81000  19.95000  18.71000  19.88000   19814300   19.88000\n",
       "2016-05-11  19.50000  19.85000  19.12000  19.28000   23742200   19.28000\n",
       "2016-05-10  18.86000  19.17000  18.23000  19.07000   20858200   19.07000\n",
       "2016-05-09  18.62000  18.85000  17.89000  18.67000   22525400   18.67000\n",
       "2016-05-06  20.49000  20.49000  18.69000  18.70000   40962500   18.70000\n",
       "2016-05-05  20.15000  20.57000  20.01000  20.49000   15216600   20.49000\n",
       "2016-05-04  20.80000  21.10000  20.20000  20.42000   22555400   20.42000\n",
       "2016-05-03  20.60000  20.99000  19.90000  20.99000   35319300   20.99000\n",
       "2016-05-02  20.61000  20.61000  20.61000  20.61000          0   20.61000\n",
       "2016-04-29  20.20000  20.77000  19.85000  20.61000   17845800   20.61000\n",
       "2016-04-28  21.49000  21.50000  19.60000  20.40000   41130000   20.40000\n",
       "2016-04-27  21.25000  22.08000  21.12000  21.44000   31398800   21.44000\n",
       "2016-04-26  20.90000  21.27000  20.41000  21.13000   19271100   21.13000\n",
       "2016-04-25  21.61000  21.61000  20.57000  20.92000   19571900   20.92000\n",
       "2016-04-22  21.44000  21.92000  21.01000  21.71000   27411200   21.71000\n",
       "2016-04-21  20.06000  21.97000  19.81000  21.42000   45631800   21.42000\n",
       "2016-04-20  20.68000  21.30000  19.42000  19.97000   34444400   19.97000\n",
       "2016-04-19  20.91000  21.23000  20.35000  20.55000   16482800   20.55000\n",
       "2016-04-18  20.55000  21.20000  19.99000  20.91000   24520700   20.91000\n",
       "2016-04-15  20.38000  20.73000  20.30000  20.71000   16271200   20.71000\n",
       "2016-04-14  20.60000  20.83000  19.92000  20.40000   26698500   20.40000\n",
       "2016-04-13  20.62000  21.17000  20.49000  20.50000   25012500   20.50000\n",
       "2016-04-12  21.50000  21.50000  20.10000  20.49000   29331800   20.49000\n",
       "2016-04-11  21.94000  22.30000  21.54000  21.57000   24064600   21.57000\n",
       "...              ...       ...       ...       ...        ...        ...\n",
       "2004-08-05  17.90003  18.10002  17.52999  17.56000    8750200    0.98194\n",
       "2004-08-04  17.86002  18.01999  17.49998  17.83001   10656000    0.99704\n",
       "2004-08-03  17.58001  18.12002  17.58001  17.73998   12815400    0.99201\n",
       "2004-08-02  17.44996  17.88003  17.21997  17.61002   10593500    0.98474\n",
       "2004-07-30  17.47997  17.80000  17.20997  17.50999   12284500    0.97914\n",
       "2004-07-29  17.15003  17.90003  16.99998  17.46997   15769400    0.97691\n",
       "2004-07-28  16.32001  16.95997  16.13003  16.92996    5868400    0.94671\n",
       "2004-07-27  16.90004  16.99998  15.99998  16.30000    7869100    0.91148\n",
       "2004-07-26  17.19996  17.21997  16.55000  16.80000    7509400    0.93944\n",
       "2004-07-23  17.01999  17.24998  16.62002  17.21997    8398200    0.96293\n",
       "2004-07-22  17.71997  17.94996  16.91004  17.02999    8947200    0.95230\n",
       "2004-07-21  17.93996  18.16004  17.59001  17.60002    9462400    0.98418\n",
       "2004-07-20  17.99998  18.32001  17.74998  17.86002   16607900    0.99872\n",
       "2004-07-19  17.39003  18.33001  17.23998  18.05000   23611100    1.00934\n",
       "2004-07-16  16.51999  17.67004  16.45997  17.40003   20279700    0.97300\n",
       "2004-07-15  16.81000  17.22997  16.50999  16.70997    9443200    0.93441\n",
       "2004-07-14  16.69997  16.90004  16.15004  16.89003    9665300    0.94448\n",
       "2004-07-13  16.12002  16.80000  15.80000  16.80000   10745200    0.93944\n",
       "2004-07-12  17.35002  17.35002  16.20997  16.42996   17923100    0.91875\n",
       "2004-07-09  17.63003  18.17996  17.31000  17.47997   10998800    0.97747\n",
       "2004-07-08  17.17004  17.66004  17.10002  17.49998    7263400    0.97858\n",
       "2004-07-07  17.80000  17.80000  17.13003  17.27999   13155000    0.96628\n",
       "2004-07-06  17.80000  18.24998  17.51999  17.85002   17018000    0.99816\n",
       "2004-07-05  17.49998  17.93996  17.12002  17.73998   17707600    0.99201\n",
       "2004-07-02  18.76999  19.10002  16.99998  17.67996   37618100    0.98865\n",
       "2004-07-01  18.90003  19.35002  18.65003  18.76999   32947700    1.04960\n",
       "2004-06-30  18.25998  19.99998  18.25998  19.02999   54561100    1.06414\n",
       "2004-06-29  20.27999  20.27999  20.27999  20.27999    3475700    1.13404\n",
       "2004-06-28  22.52999  22.99998  22.52999  22.52999    7168200    1.25986\n",
       "2004-06-25  28.10001  29.99997  23.99997  25.02999  177992600    1.39966\n",
       "\n",
       "[3057 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadir = 'yahoo-data'\n",
    "fname = '002001.csv'\n",
    "data = pd.read_csv(os.path.join(datadir, fname), index_col='Date', parse_dates=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-05-20    19.34000\n",
       "2016-05-19    18.98000\n",
       "2016-05-18    19.23000\n",
       "2016-05-17    19.77000\n",
       "2016-05-16    19.62000\n",
       "2016-05-13    19.40000\n",
       "2016-05-12    19.88000\n",
       "2016-05-11    19.28000\n",
       "2016-05-10    19.07000\n",
       "2016-05-09    18.67000\n",
       "2016-05-06    18.70000\n",
       "2016-05-05    20.49000\n",
       "2016-05-04    20.42000\n",
       "2016-05-03    20.99000\n",
       "2016-05-02    20.61000\n",
       "2016-04-29    20.61000\n",
       "2016-04-28    20.40000\n",
       "2016-04-27    21.44000\n",
       "2016-04-26    21.13000\n",
       "2016-04-25    20.92000\n",
       "2016-04-22    21.71000\n",
       "2016-04-21    21.42000\n",
       "2016-04-20    19.97000\n",
       "2016-04-19    20.55000\n",
       "2016-04-18    20.91000\n",
       "2016-04-15    20.71000\n",
       "2016-04-14    20.40000\n",
       "2016-04-13    20.50000\n",
       "2016-04-12    20.49000\n",
       "2016-04-11    21.57000\n",
       "                ...   \n",
       "2004-08-05     0.98194\n",
       "2004-08-04     0.99704\n",
       "2004-08-03     0.99201\n",
       "2004-08-02     0.98474\n",
       "2004-07-30     0.97914\n",
       "2004-07-29     0.97691\n",
       "2004-07-28     0.94671\n",
       "2004-07-27     0.91148\n",
       "2004-07-26     0.93944\n",
       "2004-07-23     0.96293\n",
       "2004-07-22     0.95230\n",
       "2004-07-21     0.98418\n",
       "2004-07-20     0.99872\n",
       "2004-07-19     1.00934\n",
       "2004-07-16     0.97300\n",
       "2004-07-15     0.93441\n",
       "2004-07-14     0.94448\n",
       "2004-07-13     0.93944\n",
       "2004-07-12     0.91875\n",
       "2004-07-09     0.97747\n",
       "2004-07-08     0.97858\n",
       "2004-07-07     0.96628\n",
       "2004-07-06     0.99816\n",
       "2004-07-05     0.99201\n",
       "2004-07-02     0.98865\n",
       "2004-07-01     1.04960\n",
       "2004-06-30     1.06414\n",
       "2004-06-29     1.13404\n",
       "2004-06-28     1.25986\n",
       "2004-06-25     1.39966\n",
       "Name: Adj Close, Length: 3057, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 使用 resample 针对复权收盘价进行重采样\n",
    "adj_price = data['Adj Close']\n",
    "adj_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E+maLWn5Cb7YLEWHpXZPDeo0jGnFAZ7qU1YfJZqMa6FqsNr/yrcFw9ksrkJ2W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\nExzQGWMsTvw/CJ/5djGGDjUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0fcecf470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adj_price.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/python/Anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).ohlc()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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rjWRydo1wycuh1lOOiUTizHGHnW5U4ihWj9JBOIz7gw8YnxVHQzRW29gd0Lse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\nAxkKWOanaL7gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0fd414780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampled = adj_price.resample('m', how='ohlc')\n",
    "resampled\n",
    "resampled.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-06-30    0.315297\n",
       "2004-07-31    0.151534\n",
       "2004-08-31    0.279060\n",
       "2004-09-30    0.283041\n",
       "2004-10-31    0.236548\n",
       "2004-11-30    0.099666\n",
       "2004-12-31    0.147270\n",
       "2005-01-31    0.253885\n",
       "2005-02-28    0.266861\n",
       "2005-03-31    0.269335\n",
       "2005-04-30    0.129689\n",
       "2005-05-31    0.303518\n",
       "2005-06-30    0.257886\n",
       "2005-07-31    0.139452\n",
       "2005-08-31    0.261976\n",
       "2005-09-30    0.075246\n",
       "2005-10-31    0.340526\n",
       "2005-11-30    0.131639\n",
       "2005-12-31    0.071430\n",
       "2006-01-31    0.141301\n",
       "2006-02-28    0.173618\n",
       "2006-03-31    0.151135\n",
       "2006-04-30    0.127417\n",
       "2006-05-31    0.268993\n",
       "2006-06-30    0.166040\n",
       "2006-07-31    0.247421\n",
       "2006-08-31    0.150091\n",
       "2006-09-30    0.093990\n",
       "2006-10-31    0.156016\n",
       "2006-11-30    0.163225\n",
       "                ...   \n",
       "2013-12-31    0.076190\n",
       "2014-01-31    0.128657\n",
       "2014-02-28    0.115819\n",
       "2014-03-31    0.213381\n",
       "2014-04-30    0.153409\n",
       "2014-05-31    0.071102\n",
       "2014-06-30    0.084388\n",
       "2014-07-31    0.189517\n",
       "2014-08-31    0.086587\n",
       "2014-09-30    0.037749\n",
       "2014-10-31    0.086667\n",
       "2014-11-30    0.058001\n",
       "2014-12-31    0.098119\n",
       "2015-01-31    0.097480\n",
       "2015-02-28    0.057931\n",
       "2015-03-31    0.161143\n",
       "2015-04-30    0.215367\n",
       "2015-05-31    0.305863\n",
       "2015-06-30    0.639591\n",
       "2015-07-31    0.239018\n",
       "2015-08-31    0.496459\n",
       "2015-09-30    0.230830\n",
       "2015-10-31    0.140940\n",
       "2015-11-30    0.158687\n",
       "2015-12-31    0.200514\n",
       "2016-01-31    0.331298\n",
       "2016-02-29    0.383070\n",
       "2016-03-31    0.209355\n",
       "2016-04-30    0.090636\n",
       "2016-05-31    0.124264\n",
       "Freq: M, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret = (resampled.high - resampled.low) / resampled.low\n",
    "ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J4mZ0LtURTM1R2HtwPB8Nx4/ZjQdPqMaH2ecsfVdkmSMALks/Xwkfi4Ex9r8y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\nN3KB268AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0fce0c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19910562305507182"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 增长曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 600690.ss 000951.sz 002001.sz\n",
    "stockid = '600690.sz'\n",
    "stockfile = '600690.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-20</th>\n",
       "      <td>8.74</td>\n",
       "      <td>9.15</td>\n",
       "      <td>8.74</td>\n",
       "      <td>9.14</td>\n",
       "      <td>55390400</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-19</th>\n",
       "      <td>8.84</td>\n",
       "      <td>9.05</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.84</td>\n",
       "      <td>34785900</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-18</th>\n",
       "      <td>8.82</td>\n",
       "      <td>8.93</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.88</td>\n",
       "      <td>44254300</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-17</th>\n",
       "      <td>9.08</td>\n",
       "      <td>9.08</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.83</td>\n",
       "      <td>42392200</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-16</th>\n",
       "      <td>8.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>8.80</td>\n",
       "      <td>9.07</td>\n",
       "      <td>59749500</td>\n",
       "      <td>9.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open  High   Low  Close    Volume  Adj Close\n",
       "Date                                                    \n",
       "2016-05-20  8.74  9.15  8.74   9.14  55390400       9.14\n",
       "2016-05-19  8.84  9.05  8.81   8.84  34785900       8.84\n",
       "2016-05-18  8.82  8.93  8.65   8.88  44254300       8.88\n",
       "2016-05-17  9.08  9.08  8.82   8.83  42392200       8.83\n",
       "2016-05-16  8.90  9.08  8.80   9.07  59749500       9.07"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = pd.read_csv(os.path.join('yahoo-data', stockfile), index_col='Date', parse_dates=True)\n",
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x1116e46d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9ErF2T7D226++iv9y8OST3njiIG21VXx+/vzn6HOammx4z6BB6e+30Ua2SL3k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11160dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adj_price = ds['Adj Close']\n",
    "adj_price.plot(figsize=(8, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 增长倍数\n",
    "\n",
    "#### 最大增长倍数及最大年化复合增长率\n",
    "\n",
    "计算最低价和最高价之间的收盘价比较，以及增长的倍数和年化复全增长率，这个反应的是一个股票最好的情况下的投资收益情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1113.2977809591985"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最高增长倍数\n",
    "total_max_growth = adj_price.max() / adj_price.min()\n",
    "total_max_growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3966150915746656"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最大年均复合增长率\n",
    "min_date = adj_price.argmin()\n",
    "max_date = adj_price.argmax()\n",
    "max_growth_per_year = total_max_growth ** (1.0 / (max_date.year - min_date.year))\n",
    "max_growth_per_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 当前增长倍数及复合增长率\n",
    "\n",
    "计算上市时的收盘价与当前的收盘价比较，增长的倍数和年化复全增长率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180.205047318612"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当前平均增长倍数\n",
    "total_growth = adj_price.ix[0] / adj_price.ix[-1]\n",
    "total_growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2533628673066715"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 年复合增长倍数\n",
    "old_date = adj_price.index[-1]\n",
    "now_date = adj_price.index[0]\n",
    "growth_per_year = total_growth ** (1.0 / (now_date.year - old_date.year))\n",
    "growth_per_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 平均年化增长率\n",
    "\n",
    "计算每年的增长率，然后再求平均值。也可以计算每月的增长率，再求平均值，可以看到更短的一些周期变化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004     0.74428\n",
       "2005     1.02725\n",
       "2006     1.17932\n",
       "2007     7.73101\n",
       "2008     6.61800\n",
       "2009    13.43369\n",
       "2010    14.32263\n",
       "2011    11.43682\n",
       "2012    11.39836\n",
       "2013    12.21995\n",
       "2014    14.97262\n",
       "2015    17.44000\n",
       "2016    19.34000\n",
       "Freq: A-DEC, Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_in_years = adj_price.to_period(freq='A').groupby(level=0).first()\n",
    "price_in_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0fd41d0f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ur+AiXQ0vImFEwS7SRkvzfCTGG+eNVTe8iIQPBbtIG/j9jqV5hZw+Ip3ULtEz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\niIhIFFGwi4iIRBEFu4iISBT5/yiCoYeQ2537AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0fd3fc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_in_years.plot(figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004         NaN\n",
       "2005    0.380193\n",
       "2006    0.148036\n",
       "2007    5.555481\n",
       "2008   -0.143967\n",
       "2009    1.029872\n",
       "2010    0.066172\n",
       "2011   -0.201486\n",
       "2012   -0.003363\n",
       "2013    0.072080\n",
       "2014    0.225260\n",
       "2015    0.164793\n",
       "2016    0.108945\n",
       "Freq: A-DEC, Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这里的关键信息：\n",
    "# 计算年化收益率时，diff 应该要除以前一年的价格，即在前一年的价格的基础上上涨了多少，而不是在当前年的价格。\n",
    "diff = price_in_years.diff()\n",
    "# diff\n",
    "rate_in_years =  diff / (price_in_years - diff)\n",
    "rate_in_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6168346678782504"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_in_years.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111e06290>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sKSK3nGFSX1tU49hy9gphGou0Bdroe3MrVZJ684MvNBaPPvrQercwc9xtL00Pw1maEv5D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\nSZIkSZIkSZIkSZIkSZIkSZIkSeP0L3zIPOJHHAkyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ced1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rate_in_years.plot(kind='bar', figsize=(8,6))\n",
    "X = [0, len(rate_in_years)]\n",
    "Y = [0, 0]\n",
    "plt.plot(X, Y, color='red', linestyle='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
